Healthcare IoT Sensor Monitoring Dataset - Remote Patient Health Tracking

Dataset Overview

This dataset simulates sensor data collected from wearable devices in an Internet of Things (IoT)-based healthcare system. The data corresponds to patient health monitoring with sensors that measure various vital signs in real-time for remote healthcare monitoring and early disease detection.

Key Features & Columns

• Patient_ID: Unique identifier for each patient (10,000 patients)
• Timestamp: Date and time of sensor reading (YYYY-MM-DD HH:MM:SS)
• Sensor_ID: Unique identifier for the IoT sensor device
• Sensor_Type: Type of sensor (Temperature, Blood Pressure, Heart Rate)
• Temperature (°C): Body temperature in degrees Celsius (35.0 - 42.0°C)
• Systolic_BP (mmHg): Systolic blood pressure (90 - 200 mmHg)
• Diastolic_BP (mmHg): Diastolic blood pressure (60 - 130 mmHg)
• Heart_Rate (bpm): Heart rate in beats per minute (40 - 150 bpm)
• Device_Battery_Level (%): Battery percentage of IoT device (0 - 100%)
• Target_Blood_Pressure: Predefined target blood pressure for monitoring
• Target_Heart_Rate: Predefined target heart rate for monitoring
• Target_Health_Status: Health status label (Healthy, Under Observation, Critical)
• Battery_Level (%): Actual battery level of the sensor device

Use Cases

• Remote patient health monitoring systems
• Vital sign anomaly detection using machine learning
• Predictive healthcare analytics and early warning systems
• IoT device battery optimization algorithms
• Time-series forecasting for patient health trends
• Classification of patient health status (Normal/Critical)
• Wearable health device performance evaluation

Machine Learning Applications

• Supervised classification: Health status prediction
• Anomaly detection: Identifying abnormal vital signs
• Time-series analysis: Heart rate and temperature trends
• Regression: Predicting future vital sign values
• Clustering: Patient risk group segmentation

Data Quality

This is a simulated dataset with clean, structured data ideal for educational purposes and machine learning experimentation. The data includes realistic ranges for all vital signs based on medical standards.